Specific attributes of products have been studied about consumer characteristics and how these factors directly impact the food production chain. Due to the barriers to penetration in a market segment, the knowledge of consumer preferences is important to enable adaptation of effective commercial strategies. Utilizing neuromarketing, electroencephalograms (EEGs), and principal component analysis (PCA), this study investigates the cognitive responses of 20 to 29-year-olds to samples of functional foods and correlates this information with purchase decisions. Brain responses were monitored using an EEG while participants tasted eight functional foods. Analysis demonstrated that important factors in deciding whether to buy or not included, after PCA processing, low beta and low gamma frequencies as well as attention and meditation percentages. The results showed that 0.96 of consumers were accurately identified as favoring a specific food and flavors, were accurately classified as bitter, sour, sweet, or salty with over 0.91 accuracy. These findings indicate that the key to effective strategies in the marketing of functional foods lies in aligning the sensory, emotional and brain attributes with consumer expectations.

This study aims to investigate the relationship between consumer neuroscience and neuromarketing using a multivariate methodology. Tools such as Principal Component Analysis (PCA) and deep learning neural networks were employed to interpret consumer responses to functional products. To this end, EEG signals were collected, recorded, and analyzed from 83 participants aged 20 to 29 to identify significant neural markers related to food consumption decisions. Key factors influencing decision making were identified, including low beta and gamma frequency bands. Participants’ levels of attention and reflection also played a role. The findings validate the effectiveness of the proposed method, demonstrating its applicability in various fields requiring accurate and reliable classification. Furthermore, some possible applications of this topic are mentioned in the food industry section, with the aim of enabling them to develop personalized nutritional strategies based on the results obtained from the brain activity of consumers.

Dado el cada vez mayor interés en materia de conciencia de la contaminación en el ambiente y sus repercusiones en la salud pública, se sugiere la creación de una aplicación con inteligencia artificial en donde se monitoricen niveles de contaminación en el Estado de México, cuyo enfoque es identificar relaciones entre factores ambientales y de salud, y brindar capacidades para el diseño de políticas públicas efectivas. Lo anterior, implica la integración de bases de datos socioeconómicos, de carencia, ambientales y de salud, la aplicación de IA de modelado predictivo, y el examen de análisis cualitativos en gobernanza y activismo social. Como resultado, se expone que el uso de alta tecnología simplifica la identificación de áreas en peligro y fomenta la participación pública en tácticas y estrategias para la gestión en la ambiente, por lo que se puede deducir que el proyecto propuesto tiene un destacado potencial positivo en la aporte y replicabilidad.

Obesity and diabetes are leading contributors to cardiovascular disease, driven largely by ultra-processed diets. In this work, we propose a deep learning framework to analyze EEG patterns in functional food consumption, using Principal Component Analysis (PCA) to extract features and a neural network architecture to model consumer decision-making. Our supervised learning approach predicts taste preferences for a unique consumer group without prior ratings, demonstrating that decision making can be effectively modeled using neural signals. By optimizing the hyperparameters, we achieve a 1.23% improvement in the efficiency of the model, providing a scalable solution to understand the diet choices. This work bridges neuroscience and machine learning, offering information on consumer behavior with potential applications in personalized nutrition and health-aware food recommendation systems.

Los efectos temporales de las estrategias con poca efectividad para atraer clientes a las empresas y los costos que estas afrontan frente a símbolos y / o personajes de moda con períodos cortos. En este contexto, el propósito de esta investigación científica es obtener información sobre los factores que muestran mayor influencia dentro del proceso de compra en el segmento infantil proporcionando una estrategia de marketing y, en consecuencia, ayudando a acortar los costos que las empresas obtienen mientras pagan cantidades excesivas a corto plazo para atraer clientes. El propósito del estudio es investigar y presentar pruebas de los factores que influyen en la decisión de compra presentando un modelo de comportamiento de Marketing Mix que comienza con la investigación exploratoria y abarca estrategias comerciales que se relacionan con el trabajo.. Este estudio es una investigación exploratoria realizada por el autor de esta tesis a través de entrevistas semiestructuradas. Los hallazgos se. Se analizan con un marco teórico derivado de la investigación exploratoria y con el uso de técnicas seleccionadas de la literatura. Se respalda la categorización de la información. Presenta una propuesta de estrategias basadas en valores y relaciones afectivas, y sugiere estrategias para los valores de liderazgo basadas en la principio de estilo de liderazgo de autenticidad.